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Record W2292482263 · doi:10.1586/17476348.2016.1155455

Current methods to diagnose small airway disease in patients with COPD

2016· article· en· W2292482263 on OpenAlexaff
Fernando Sergio Leitão Filho, Hao Hang Chen, David A. Ngan, Anthony Raymond Tam, Miranda Kirby, Don D. Sin

Bibliographic record

VenueExpert Review of Respiratory Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineCOPDIntensive care medicineAirwayPulmonary diseaseDiseaseInternal medicineSurgery

Abstract

fetched live from OpenAlex

The small airways are characterized by an internal diameter < 2 mm and absence of cartilage. Approximately 10-25% of total airway resistance in healthy lungs is due to the small airways, with their contribution to total airway resistance increasing substantially in chronic obstructive pulmonary disease (COPD). As the small airways are located in the lung periphery, they are not easily evaluable, which can potentially interfere with the diagnosis (especially at early stages), monitoring, detection of responses to clinical interventions, and prognostic evaluation in COPD. Here, we will discuss the currently available methods in clinical practice to evaluate small airway disease in COPD, focusing on the concept, advantages, and disadvantages of each method.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.705
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.401
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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